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Open problems in Character recognition OCR

Evaluating dense visual text rendering in generated images2 papers raise it · 2 address it
Current OCR and VLM metrics fail to catch local rendering defects like extra or missing strokes in generated text.
- UltraText Bench: A Comprehensive Bilingual Benchmark for Evaluating Visual Text Rendering in Image Generationraises · addresses
- TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Renderingraises · addresses
Unified OCR models for recognition, parsing, and reasoning1 paper raises it · 1 addresses it
Existing OCR systems excel at isolated tasks but struggle to balance recognition, parsing, and reasoning across complex visual scenes.
- PolyOCR-Venus: Unified OCR Foundation Models for Text-Centric Visual Intelligenceraises · addresses
Severity-aware OCR error metrics1 paper raises it · 1 addresses it
Standard metrics like CER treat all errors equally, ignoring the real-world impact of different mistakes.
- Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCRraises · addresses
Low-resource and archival OCR robustness2 papers raise it · 1 addresses it
OCR systems perform poorly on low-resource languages, degraded scans, and realistic segmentation noise in archival contexts.
- Design Considerations in Low-Resource Archival OCR: A Case Study in Lakotaraises · addresses
- Evaluating Cloud and Self-Hosted OCR Systems for Arabic, English, and French Documents: Bounding Box Detection, Text Recognition, and Mathematical Expression Recognitionraises
Human-in-the-loop OCR annotation efficiency1 paper raises it · 1 addresses it
Manual document annotation is labor-intensive, and automation often fails to redirect human effort effectively toward review and correction.
- GAIDA: Generative AI for Human-in-the-Loop Document Annotationraises · addresses

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character recognition ocr · recognition ocr · character recognition75 papers15 this week

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arXiv cs.CLCodeOct 14▲ 2
Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCR
Nguyen Nguyen, Yongyi Zang
Character Error Rate (CER), the standard metric for OCR and vision-language models (VLMs), charges a typo as much as a wrong name. We introduce semCER, which keeps CER's alignment and denominator and charges each grapheme edit a severity from an LLM judge, with no word segmentation. Normalized CER keeps the score of a whole transcription, where it tracks readers as well as semCER or better, and semCER breaks that score down into named edits. The charges track readers' ratings edit by edit in English, Vietnamese, and Chinese and put the edits readers rate most severe near the top of a sorted list. On pairs drawn where CER and semCER disagreed, readers side with semCER more often than with normalized CER in English and Chinese, and equally often in Vietnamese. Across 16 languages, the judge charges a wrong diacritic or tone mark almost nothing in Spanish but nearly a wrong letter's severity in Telugu. Where we can count real words, a general VLM's wrong letter forms one far above chance while a classical pipeline's stays near it. We release the 61 million judged edits, the aggregated reader ratings, and the distilled scorer at https://github.com/nguyenx2t/semcer.
PDF arXiv Code
arXiv cs.CVOct 14
Read, Parse, Describe: Unified Document Parsing with Visual Element Description Generation
Yuheng Chen, Yufan Chen, Zhuojun Cai, Ruiping Liu, Junwei Zheng +6
Document understanding systems are typically evaluated as separate components via layout analysis, optical text recognition, reading-order prediction, and image captioning. This separation leaves scientific figures, captions, and visual semantics weakly connected, and fails to measure whether a model can produce a single page-level parse that is both structurally faithful and semantically accessible. We introduce Read-Parse-Describe, a unified dataset for document parsing with visual element description generation. Given a page image, models are required to output an ordered list of layout elements containing bounding boxes, semantic labels, reading-order indices, recognized textual content, and alternative text descriptions for visual elements. The benchmark is built from 2,402 documents, comprising 18,163 continuous pages, 125,218 layout elements, and 11,338 visual instances. Besides, we further propose a plug-and-play space-awareness enhancer that reuses multi-layer visual features and injects grid-aware spatial guidance into vision-language tokens for layout-aware generation. Experiments across end-to-end VLMs and pipeline parsers show that existing systems still struggle to…
PDF arXiv
arXiv cs.CVOct 14▲ 9
TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Rendering
Yiyang Chen, Guo-Hua Wang, Yaoli Liu, Qing-Guo Chen, Zhao Xu +2
Text-to-image models increasingly generate readable Chinese text that still contains local rendering defects, such as a character with an extra or missing stroke or a malformed component. Such errors are invisible to OCR and vision-language models, which are trained for recognition and are therefore invariant to the very structural deviations that define a rendering error. We formalize text rendering verification as a target-conditioned problem: given a target character and an image crop, decide whether the crop is a correct rendering of that character. Our verifier TEXTVERIFIER feeds the text tower a character's Ideographic Description Sequence (IDS) rather than a single-character symbol, and is trained on 12.7M crop-IDS pairs plus label-free "same-character, different-quality" hard pairs sampled from the training trajectory of a text-region inpainting model. To evaluate this ability we build TEXTRENDERBENCH-ZH, the first per-character benchmark for generated Chinese text, annotating position, identity, and correctness for 7,358 characters. TEXTVERIFIER reaches an F1 of 0.844, far above OCR confidence (0.526), a general VLM (0.329), and TextPecker (0.590). Used as a…
PDF arXiv
arXiv cs.CVOct 13
AIRRI: Towards a Human-Readable, Machine-Resistant Reading Interface
Emory Salaun, Syed Ayon, Kaleel Mahmood, Shaun Wallace
The rapid advancement of image-to-text extraction systems and large language models (LLMs) poses new challenges for educational assessment by making assignment materials easy to transcribe and solve with minimal human effort. To address this problem, we present AIRRI (Artificial Intelligence Resilient Reading Interface), a framework for generating machine-resistant educational content, a dataset of higher-education assignments for evaluation, and an initial evaluation of the human readability of AIRRI machine-resistant text designs. AIRRI employs two categories of machine-resistance techniques. First, AIRRI applies image transformations including character spacing, word spacing, line spacing, character opacity variation, stripe overlays, and image noise. Second, AIRRI applies decision-based adversarial attacks: RayS, SurFree, and ADBA. We evaluate AIRRI against three image-to-text systems: Tesseract, EasyOCR, and the transformer-based GOT-OCR2 model. Our results show that AIRRI substantially degrades automated text extraction performance. AIRRI increased character error rates for EasyOCR (6.9% to 55.9%), Tesseract (2.3% to 100.0%), and GOT-OCR2 (30.7% to 48.1%). Word error rates…
PDF arXiv
arXiv cs.CVOct 13
Heading Hierarchy Reconstruction for Complex and Long Documents with Level-Wise Multimodal Memory
Qian Chen, Xianyin Zhang, Lifan Guo, Chi Zhang
Vision-language models (VLMs) have been widely adopted for document parsing and optical character recognition (OCR), as they can accurately extract page-level elements. However, downstream applications such as retrieval-augmented generation (RAG) also require coherent document-level structure. Although these models indicate heading levels using different numbers of preceding ``\#'' symbols, page-by-page prediction often lacks document-level consistency. In practice, input-resolution and context-length constraints make it infeasible for a VLM to jointly process all page images in documents containing tens or hundreds of pages. To address this limitation, we propose a VLM-based framework that incrementally reconstructs heading hierarchies with a \emph{level-wise multimodal memory}. The memory retains a compact subset of all previously processed pages, selected by anchors. Our framework supports flexible anchor selection, and in this work we select the most recent node at each level, allowing the VLM to observe the visual, textual, and layout patterns of headings at every level. For comprehensive evaluation, we also introduce the Complex and Long Document Heading Hierarchy…
PDF arXiv
arXiv cs.CLOct 13
Text-Only vs. Image-Aware VLM Judges for Manga Translation Evaluation
Subaru Kimura, Jun Suzuki
In manga translation, visual context outside speech-bubble text, including speaker identities, referents, and scene tone, is depicted in page images, making it natural to refer to images during translation evaluation. However, most conventional manga translation evaluations rely solely on text-only automatic metrics, leaving the impact of image input insufficiently explored. We propose a practical validation framework that employs the same vision-language model (VLM) as both a text-only judge (TOJ) and an image-aware judge (IAJ) to investigate evaluation divergences associated with image-aware evaluation procedures. We use a Japanese-English manga dataset of official translations aligned at the page and speech-bubble levels. First, adding page images to TOJ improved the primary VLM's detection of deliberately injected, visually dependent degradations. Second, we analyzed evaluation divergences between TOJ and IAJ, finding different error counts and error-category distributions. Human verification did not reveal a consistent preference between TOJ and IAJ.
PDF arXiv

Papers

Topics in this field

character recognition ocr · recognition ocr · character recognition75 papers15 this week

Active contributors · 90 days

Recent papers

All in Research →

arXiv cs.CLCodeOct 14▲ 2
Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCR
Nguyen Nguyen, Yongyi Zang
Character Error Rate (CER), the standard metric for OCR and vision-language models (VLMs), charges a typo as much as a wrong name. We introduce semCER, which keeps CER's alignment and denominator and charges each grapheme edit a severity from an LLM judge, with no word segmentation. Normalized CER keeps the score of a whole transcription, where it tracks readers as well as semCER or better, and semCER breaks that score down into named edits. The charges track readers' ratings edit by edit in English, Vietnamese, and Chinese and put the edits readers rate most severe near the top of a sorted list. On pairs drawn where CER and semCER disagreed, readers side with semCER more often than with normalized CER in English and Chinese, and equally often in Vietnamese. Across 16 languages, the judge charges a wrong diacritic or tone mark almost nothing in Spanish but nearly a wrong letter's severity in Telugu. Where we can count real words, a general VLM's wrong letter forms one far above chance while a classical pipeline's stays near it. We release the 61 million judged edits, the aggregated reader ratings, and the distilled scorer at https://github.com/nguyenx2t/semcer.
PDF arXiv Code
arXiv cs.CVOct 14
Read, Parse, Describe: Unified Document Parsing with Visual Element Description Generation
Yuheng Chen, Yufan Chen, Zhuojun Cai, Ruiping Liu, Junwei Zheng +6
Document understanding systems are typically evaluated as separate components via layout analysis, optical text recognition, reading-order prediction, and image captioning. This separation leaves scientific figures, captions, and visual semantics weakly connected, and fails to measure whether a model can produce a single page-level parse that is both structurally faithful and semantically accessible. We introduce Read-Parse-Describe, a unified dataset for document parsing with visual element description generation. Given a page image, models are required to output an ordered list of layout elements containing bounding boxes, semantic labels, reading-order indices, recognized textual content, and alternative text descriptions for visual elements. The benchmark is built from 2,402 documents, comprising 18,163 continuous pages, 125,218 layout elements, and 11,338 visual instances. Besides, we further propose a plug-and-play space-awareness enhancer that reuses multi-layer visual features and injects grid-aware spatial guidance into vision-language tokens for layout-aware generation. Experiments across end-to-end VLMs and pipeline parsers show that existing systems still struggle to…
PDF arXiv
arXiv cs.CVOct 14▲ 9
TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Rendering
Yiyang Chen, Guo-Hua Wang, Yaoli Liu, Qing-Guo Chen, Zhao Xu +2
Text-to-image models increasingly generate readable Chinese text that still contains local rendering defects, such as a character with an extra or missing stroke or a malformed component. Such errors are invisible to OCR and vision-language models, which are trained for recognition and are therefore invariant to the very structural deviations that define a rendering error. We formalize text rendering verification as a target-conditioned problem: given a target character and an image crop, decide whether the crop is a correct rendering of that character. Our verifier TEXTVERIFIER feeds the text tower a character's Ideographic Description Sequence (IDS) rather than a single-character symbol, and is trained on 12.7M crop-IDS pairs plus label-free "same-character, different-quality" hard pairs sampled from the training trajectory of a text-region inpainting model. To evaluate this ability we build TEXTRENDERBENCH-ZH, the first per-character benchmark for generated Chinese text, annotating position, identity, and correctness for 7,358 characters. TEXTVERIFIER reaches an F1 of 0.844, far above OCR confidence (0.526), a general VLM (0.329), and TextPecker (0.590). Used as a…
PDF arXiv
arXiv cs.CVOct 13
AIRRI: Towards a Human-Readable, Machine-Resistant Reading Interface
Emory Salaun, Syed Ayon, Kaleel Mahmood, Shaun Wallace
The rapid advancement of image-to-text extraction systems and large language models (LLMs) poses new challenges for educational assessment by making assignment materials easy to transcribe and solve with minimal human effort. To address this problem, we present AIRRI (Artificial Intelligence Resilient Reading Interface), a framework for generating machine-resistant educational content, a dataset of higher-education assignments for evaluation, and an initial evaluation of the human readability of AIRRI machine-resistant text designs. AIRRI employs two categories of machine-resistance techniques. First, AIRRI applies image transformations including character spacing, word spacing, line spacing, character opacity variation, stripe overlays, and image noise. Second, AIRRI applies decision-based adversarial attacks: RayS, SurFree, and ADBA. We evaluate AIRRI against three image-to-text systems: Tesseract, EasyOCR, and the transformer-based GOT-OCR2 model. Our results show that AIRRI substantially degrades automated text extraction performance. AIRRI increased character error rates for EasyOCR (6.9% to 55.9%), Tesseract (2.3% to 100.0%), and GOT-OCR2 (30.7% to 48.1%). Word error rates…
PDF arXiv
arXiv cs.CVOct 13
Heading Hierarchy Reconstruction for Complex and Long Documents with Level-Wise Multimodal Memory
Qian Chen, Xianyin Zhang, Lifan Guo, Chi Zhang
Vision-language models (VLMs) have been widely adopted for document parsing and optical character recognition (OCR), as they can accurately extract page-level elements. However, downstream applications such as retrieval-augmented generation (RAG) also require coherent document-level structure. Although these models indicate heading levels using different numbers of preceding ``\#'' symbols, page-by-page prediction often lacks document-level consistency. In practice, input-resolution and context-length constraints make it infeasible for a VLM to jointly process all page images in documents containing tens or hundreds of pages. To address this limitation, we propose a VLM-based framework that incrementally reconstructs heading hierarchies with a \emph{level-wise multimodal memory}. The memory retains a compact subset of all previously processed pages, selected by anchors. Our framework supports flexible anchor selection, and in this work we select the most recent node at each level, allowing the VLM to observe the visual, textual, and layout patterns of headings at every level. For comprehensive evaluation, we also introduce the Complex and Long Document Heading Hierarchy…
PDF arXiv
arXiv cs.CLOct 13
Text-Only vs. Image-Aware VLM Judges for Manga Translation Evaluation
Subaru Kimura, Jun Suzuki
In manga translation, visual context outside speech-bubble text, including speaker identities, referents, and scene tone, is depicted in page images, making it natural to refer to images during translation evaluation. However, most conventional manga translation evaluations rely solely on text-only automatic metrics, leaving the impact of image input insufficiently explored. We propose a practical validation framework that employs the same vision-language model (VLM) as both a text-only judge (TOJ) and an image-aware judge (IAJ) to investigate evaluation divergences associated with image-aware evaluation procedures. We use a Japanese-English manga dataset of official translations aligned at the page and speech-bubble levels. First, adding page images to TOJ improved the primary VLM's detection of deliberately injected, visually dependent degradations. Second, we analyzed evaluation divergences between TOJ and IAJ, finding different error counts and error-category distributions. Human verification did not reveal a consistent preference between TOJ and IAJ.
PDF arXiv

Activity · 12 weeks

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Fields · AI · updated 52m ago

Character recognition OCR

Found by the topic model: character recognition ocr.

15papers this week−21.1% vs last week

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68 papers this month

Open problems in Character recognition OCR

Evaluating dense visual text rendering in generated images2 papers raise it · 2 address it
Current OCR and VLM metrics fail to catch local rendering defects like extra or missing strokes in generated text.
- UltraText Bench: A Comprehensive Bilingual Benchmark for Evaluating Visual Text Rendering in Image Generationraises · addresses
- TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Renderingraises · addresses
Unified OCR models for recognition, parsing, and reasoning1 paper raises it · 1 addresses it
Existing OCR systems excel at isolated tasks but struggle to balance recognition, parsing, and reasoning across complex visual scenes.
- PolyOCR-Venus: Unified OCR Foundation Models for Text-Centric Visual Intelligenceraises · addresses
Severity-aware OCR error metrics1 paper raises it · 1 addresses it
Standard metrics like CER treat all errors equally, ignoring the real-world impact of different mistakes.
- Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCRraises · addresses
Low-resource and archival OCR robustness2 papers raise it · 1 addresses it
OCR systems perform poorly on low-resource languages, degraded scans, and realistic segmentation noise in archival contexts.
- Design Considerations in Low-Resource Archival OCR: A Case Study in Lakotaraises · addresses
- Evaluating Cloud and Self-Hosted OCR Systems for Arabic, English, and French Documents: Bounding Box Detection, Text Recognition, and Mathematical Expression Recognitionraises
Human-in-the-loop OCR annotation efficiency1 paper raises it · 1 addresses it
Manual document annotation is labor-intensive, and automation often fails to redirect human effort effectively toward review and correction.
- GAIDA: Generative AI for Human-in-the-Loop Document Annotationraises · addresses

Named by an AI from these papers' abstracts.